
Artificial Intelligent Techniques for Wireless Communication and Networking
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The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field.
Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments.
Audience
Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.
R. Kanthavel, PhD is a Professor in the Dept. of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.
K. Ananthajothi, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on 'Theory of Computation and Python Programming' and holds 2 patents.
S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
R. Karthik Ganesh, PhD is an associate professor in the Dept. of Computer Science and Engineering, SCAD College of Engg. and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.
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Persons
R. Kanthavel, PhD is a Professor in the Department of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.
K. Ananthajothi, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on "Theory of Computation and Python Programming" and holds 2 patents.
S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
R. Karthik Ganesh, PhD is an associate professor in the Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.
Content
Preface xvii
1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning 1
P. Anbalagan, S. Saravanan and R. Saminathan
1.1 Introduction 2
1.2 Comprehensive Study 3
1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 7
1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 9
1.5 Conclusion 12
2 Impact of AI in 5G Wireless Technologies and Communication Systems 15
A. Sivasundari and K. Ananthajothi
2.1 Introduction 16
2.2 Integrated Services of AI in 5G and 5G in AI 18
2.3 Artificial Intelligence and 5G in the Industrial Space 23
2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 25
2.5 Conclusion 28
3 Artificial Intelligence Revolution in Logistics and Supply Chain Management 31
P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy
3.1 Introduction 32
3.2 Theory--AI in Logistics and Supply Chain Market 35
3.3 Factors to Propel Business Into the Future Harnessing Automation 40
3.4 Conclusion 43
4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning 47
M. P. Vaishnnave and R. Manivannan
4.1 Introduction 47
4.2 An Overview of Reinforcement Learning in Agriculture 49
4.3 Reinforcement Learning Startups for Crop Prediction 52
4.4 Conclusion 57
5 Cost Optimization for Inventory Management in Blockchain and Cloud 59
C. Govindasamy, A. Antonidoss and A. Pandiaraj
5.1 Introduction 60
5.2 Blockchain: The Future of Inventory Management 62
5.3 Cost Optimization for Blockchain Inventory Management in Cloud 66
5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 71
5.5 Conclusion 72
6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases 75
G. Gangadevi and C. Jayakumar
6.1 Introduction 75
6.2 Literature Review 76
6.3 Proposed Idea 82
6.4 Reference Gap 86
6.5 Conclusion 87
7 Generating Art and Music Using Deep Neural Networks 91
A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna
7.1 Introduction 91
7.2 Related Works 92
7.3 System Architecture 94
7.4 System Development 96
7.5 Algorithm-LSTM 100
7.6 Result 100
7.7 Conclusions 101
8 Deep Learning Era for Future 6G Wireless Communications--Theory, Applications, and Challenges 105
S.K.B. Sangeetha and R. Dhaya
8.1 Introduction 106
8.2 Study of Wireless Technology 108
8.3 Deep Learning Enabled 6G Wireless Communication 113
8.4 Applications and Future Research Directions 117
9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks 121
J. Banumathi, S.K.B. Sangeetha and R. Dhaya
9.1 Introduction 122
9.2 Spectrum Sensing in Cognitive Radio Networks 122
9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 124
9.4 Cooperative Sensing Among Cognitive Radios 125
9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 128
9.6 Spectrum Agile Radios: Utilization and Sensing Architectures 128
9.7 Some Fundamental Limits on Cognitive Radio 130
9.8 Cooperative Strategies and Capacity Theorems for Relay Networks 131
9.9 Research Challenges in Cooperative Communication 133
9.10 Conclusion 135
10 Natural Language Processing 139
S. Meera and S. Geerthik
10.1 Introduction 139
10.2 Conclusions 152
References 152
11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval 155
D. Sujatha, M. Subramaniam and A. Kathirvel
11.1 Introduction 156
11.2 Literature Review 158
11.3 Class Level Semantic Similarity-Based Retrieval 159
11.4 Results and Discussion 164
12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes 175
J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya
12.1 Introduction 176
12.2 Literature Survey 176
12.3 Proposed Work 177
12.4 Results 180
12.5 Conclusion and Future Work 190
13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation 193
Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna
13.1 Introduction 194
13.2 Background 196
13.3 Issues and Gap Identified 197
13.4 Main Focus of the Chapter 198
13.5 Mobility 199
13.6 Routing Protocol 201
13.7 High Altitude Platforms (HAPs) 202
13.8 Connectivity Graph Metrics 204
13.9 Aerial Vehicle Network Simulator (AVENs) 206
13.10 Conclusion 207
14 Artificial Intelligence in Logistics and Supply Chain 211
Jeyaraju Jayaprakash
14.1 Introduction to Logistics and Supply Chain 212
14.2 Recent Research Avenues in Supply Chain 217
14.3 Importance and Impact of AI 222
14.4 Research Gap of AI-Based Supply Chain 224
15 Hereditary Factor-Based Multi-Featured Algorithm for Early Diabetes Detection Using Machine Learning 235
S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan
15.1 Introduction 236
15.2 Literature Review 237
15.3 Objectives of the Proposed System 244
15.4 Proposed System 245
15.5 HIVE and R as Evaluation Tools 246
15.6 Decision Trees 247
15.7 Results and Discussions 250
15.8 Conclusion 252
16 Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism 255
V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai
16.1 Introduction 255
16.2 Related Study 258
16.3 System Model 259
16.4 Experiments and Results 264
16.5 Conclusion 267
17 Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing 269
R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumar
and R. Mahaveerakannan
17.1 Introduction 270
17.2 New Development of Artificial Intelligence 271
17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 271
17.4 Current Status and Problems of Green Manufacturing 272
17.5 Artificial Intelligence for Green Manufacturing 276
17.6 Detailed Description of Common Encryption Algorithms 280
17.7 Current and Future Works 282
17.8 Conclusion 283
18 Deep Learning in 5G Networks 287
G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani
18.1 5G Networks 287
18.2 Artificial Intelligence and 5G Networks 291
18.3 Deep Learning in 5G Networks 293
19 EIDR Umpiring Security Models for Wireless Sensor Networks 299
A. Kathirvel, S. Navaneethan and M. Subramaniam
19.1 Introduction 299
19.2 A Review of Various Routing Protocols 302
19.3 Scope of Chapter 307
19.4 Conclusions and Future Work 311
20 Artificial Intelligence in Wireless Communication 317
Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta
20.1 Introduction 318
20.2 Artificial Intelligence: A Grand Jewel Mine 318
20.3 Wireless Communication: An Overview 320
20.4 Wireless Revolution 320
20.5 The Present Times 321
20.6 Artificial Intelligence in Wireless Communication 321
20.7 Artificial Neural Network 324
20.8 The Deployment of 5G 326
20.9 Looking Into the Features of 5G 327
20.10 AI and the Internet of Things (IoT) 328
20.11 Artificial Intelligence in Software-Defined Networks (SDN) 329
20.12 Artificial Intelligence in Network Function Virtualization 331
20.13 Conclusion 332
References 332
Index 335
Preface
In the current digital era, artificial intelligence (AI) resembling human intelligence is enabling superior inventions for the advancement of the world. Broadening the scientific scope of AI has made it possible to change fundamentals and modulate everlasting facts in the wireless communication and networking domains. This breakthrough in AI miraculously preserves the inspired vision of communication technology.
Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is also elaborated. Moreover, the application side of integrated technologies are also explored to enhance AI Revolution innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments.
This book allows both practitioners and researchers to share their opinions and recent research on the convergence of these technologies with those in academia and industry. The contributors have presented their technical evaluation and comparative analysis of existing technologies; and theoretical explanations and experimental case studies related to real-time scenarios are also included. Furthermore, this book will connect IT professionals, researchers, and academicians working on 5G communication and networking technologies.
The book is organized into 20 chapters that address AI principles and techniques used in wireless communication and networking. It outlines the benefits, functions, and future role of AI in wireless communication and networking. In addition, AI applications are addressed from a variety of aspects, including basic principles and prominent methodologies that offer researchers relevant instructions to follow in their research. The editing team and expert reviewers in various disciplines have thoroughly reviewed the information included.
- - In Chapter 1, "Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning," P. Anbalagan, S. Saravanan and R. Saminathan present a brief guide to the deep reinforcement learning process and its detailed applications and research directions in order to enhance the basics of reinforcement mechanisms.
- - In Chapter 2, "Impact of AI in 5G Wireless Technologies and Communication Systems," A. Sivasundari and K. Ananthajothi present an in-depth overview of the implementation of AI to improve 5G wireless communication systems, discuss the role and difficulties faced, and highlight suggestions for future studies on integrating advanced AI into 5G wireless communications.
- - In Chapter 3, "Artificial Intelligence Revolution in Logistics and Supply Chain Management," P.J. Satish Kumar, Ratna Kamala Petla, Elangovan K and P.G. Kuppusamy give a brief description of recent developments and some relevant impacts concerning logistics and supply chain related to AI.
- - In Chapter 4, "An Empirical Study of Crop Yield Prediction Using Reinforcement Learning," M. P. Vaishnnave and R. Manivannan use reinforcement learning (RL) data technologies and high-performance computing to create new possibilities to activate, calculate, and recognize the agricultural crop forecast.
- - In Chapter 5, "Cost Optimization for Inventory Management in Blockchain Under Cloud," C. Govindasamy, A. Antonidoss and A. Pandiaraj investigate some of the most prominent bases of blockchain for inventory management of blockchain and cost optimization methods for inventory management in cloud.
- - In Chapter 6, "Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases," G. Gangadevi and C. Jayakumar describe the deep learning architectures for plant disease prediction and classification using standard techniques like artificial neural network (ANN), k-means classifier (K-means), recurrent neural network (RNN), k-nearest neighbor (K-NN) classifier, and support vector machine (SVM).
- - In Chapter 7, "Generating Art and Music Using Deep Neural Networks," A. Pandiaraj, Lakshmana Prakash, R. Gopal and P. Rajesh Kanna develop a model using deep neural nets that can imagine like humans and generate art and music of their own. This model can also be used to increase cognitive efficiency in AGI (artificial general intelligence), thereby improving the agent's image classification and object localization.
- - In Chapter 8, "Deep Learning Era for Future 6G Wireless Communications - Theory, Applications, and Challenges," S. K. B. Sangeetha, R. Dhaya, S. Gayathri, K. Kamala and S. Divya Keerthi encapsulate the background of 6G wireless communication with details on how deep learning has made a contribution to 6G wireless technology and also highlight future research directions for deep learning-driven wireless technology.
- - In Chapter 9, "Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks," J. Banumathi, S.K B. Sangeetha and R. Dhaya discuss the use of cooperative spectrum sensing in cognitive radio systems to improve the efficiency of detecting primary users.
- - In Chapter 10, "Natural Language Processing," S. Meera and B. Persis Urban Ivy elucidate natural language processing (NLP), which is a branch of computer science and artificial intelligence that studies how computers and humans communicate in natural language, with the aim of computers understanding language as well as humans do.
- - In Chapter 11, "Class-Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval," Sujatha D, Subramaniam M and A. Kathirvel present an efficient class-level multi-feature semantic similarity measure-based approach. The proposed method receives the input query and estimates class-level information similarity, class-level texture similarity, and class-level semantic similarity measures for different classes.
- - In Chapter 12, "Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling with Diurnal Changes," J. V. Anand, T. R. Ganesh Babu, R. Praveena and K. Vidhya describe an inter-depth variation profile in line with latitude and longitude in a particular area, calculate attenuations by the temperature coefficient of real data sets, and attribute attenuation to a frequency-dependent loss.
- - In Chapter 13, "Multi-Layer UAV Ad-Hoc Network Architecture, Protocol and Simulation," Kamlesh Lakhwani, Tejpreet Singh Orchu and Aruna discuss the use of flying ad hoc networks (FANETs) to provide communication among the UAVs. In FANET, the selection of a suitable mobility model is a critical task for researchers.
- - In Chapter 14, "Artificial Intelligence in Logistics and Supply Chain," Jeyaraju Jayaprakash explains the source of activities in any white product manufacturing and service industry. This logistics and supply chain network will become more complex over the next few decades as a result of pandemic situations, natural disasters, increasing population, and other side entities developing smart strategies.
- - In Chapter 15, "Hereditary Factor-Based Multi-Feature Algorithm for Early Diabetes Detection Using Machine Learning," S. Deepajothi, R. Juliana, Aruna S K and R. Thiagarajan indicate that the predominance of diabetes mellitus among the global population ultimately leads to blindness and death in some cases. The proposed model attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis.
- - In Chapter 16, "Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism," V. Sharmila, Mandal K, Shankar Shalani and P. Ezhumalai discuss opportunistic routing of an intercepted packet to provide an effective wireless mesh network. Traditional opportunistic routing algorithms are being used to provide high-speed use batching of packets, which is a complex task. Therefore, an enhanced opportunistic feedback-based algorithm is proposed in this chapter in which individual packet forwarding uses a new route calculation in the proposed work that takes into consideration the cost of transmitting feedback and the capacity of the nodes to choose appropriate rates for monitoring operating conditions.
- - In Chapter 17, "Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing," R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumar and R. Mahaveerakannan propose an efficient green manufacturing approach in SM systems with the aid of AI and cyber security frameworks. The proposed work employs a dual-stage artificial neural network (ANN) to find the design configuration of SM systems in industries. Then, for maintaining data confidentiality while communicating, the data are encrypted using the 3DES approach.
- - In Chapter 18, "Deep Learning in 5G Networks," G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani discuss a 3D-CNN model combined with RNN model for analyzing and classifying the network traffic into three...
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